Asymmetric Diffusion Recommendation Model

• URL: http://arxiv.org/abs/2508.12706v1
• Date: Mon, 18 Aug 2025 08:05:25 GMT
• Title: Asymmetric Diffusion Recommendation Model
• Authors: Yongchun Zhu, Guanyu Jiang, Jingwu Chen, Feng Zhang, Xiao Yang, Zuotao Liu,
• Abstract summary: We propose Asymmetric Diffusion Recommendation Model (AsymDiffRec), which learns forward and reverse processes in an asymmetric manner.<n>AsymDiffRec has been implemented in the Douyin Music App.
• Score: 12.48124100628083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, motivated by the outstanding achievements of diffusion models, the diffusion process has been employed to strengthen representation learning in recommendation systems. Most diffusion-based recommendation models typically utilize standard Gaussian noise in symmetric forward and reverse processes in continuous data space. Nevertheless, the samples derived from recommendation systems inhabit a discrete data space, which is fundamentally different from the continuous one. Moreover, Gaussian noise has the potential to corrupt personalized information within latent representations. In this work, we propose a novel and effective method, named Asymmetric Diffusion Recommendation Model (AsymDiffRec), which learns forward and reverse processes in an asymmetric manner. We define a generalized forward process that simulates the missing features in real-world recommendation samples. The reverse process is then performed in an asymmetric latent feature space. To preserve personalized information within the latent representation, a task-oriented optimization strategy is introduced. In the serving stage, the raw sample with missing features is regarded as a noisy input to generate a denoising and robust representation for the final prediction. By equipping base models with AsymDiffRec, we conduct online A/B tests, achieving improvements of +0.131% and +0.166% in terms of users' active days and app usage duration respectively. Additionally, the extended offline experiments also demonstrate improvements. AsymDiffRec has been implemented in the Douyin Music App.

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